Nathan Inkawhich

Orcid: 0000-0002-6429-5632

According to our database¹, Nathan Inkawhich authored at least 31 papers between 2018 and 2025.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Links

On csauthors.net:

Bibliography

2025

Multi-layer Radial Basis Function Networks for Out-of-distribution Detection.

[BibT_eX]

[DOI]

CoRR, January, 2025

2024

Comprehensive OOD Detection Improvements.

[BibT_eX]

[DOI]

CoRR, 2024

Tunable Hybrid Proposal Networks for the Open World.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

SoK: A Review of Differentially Private Linear Models For High-Dimensional Data.

[BibT_eX]

[DOI]

Amol Khanna

Edward Raff

Nathan Inkawhich

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2024

Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Multi-modal Aerial View Image Challenge: SAR Classification.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Multi-modal Aerial View Image Challenge: Sensor Domain Translation.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

OSR-ViT: A Simple and Modular Framework for Open-Set Object Detection and Discovery.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Big Data, 2024

2023

A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition.

[BibT_eX]

[DOI]

Nathan Inkawhich

IEEE Geosci. Remote. Sens. Lett., 2023

Adversarial Attacks on Foundational Vision Models.

[BibT_eX]

[DOI]

Nathan Inkawhich

Gwendolyn McDonald

Ryan Luley

CoRR, 2023

Establishing baselines and introducing TernaryMixOE for fine-grained out-of-distribution detection.

[BibT_eX]

[DOI]

Noah Fleischmann

Walter D. Bennette

Nathan Inkawhich

CoRR, 2023

SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection.

[BibT_eX]

[DOI]

CoRR, 2023

Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Multi-modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results - PBVS 2023.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Multi-modal Aerial View Object Classification Challenge Results - PBVS 2023.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification.

[BibT_eX]

[DOI]

Proceedings of the Conference on Lifelong Learning Agents, 2023

2022

Improving Out-of-Distribution Detection by Learning From the Deployment Environment.

[BibT_eX]

[DOI]

IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022

Self-Trained Proposal Networks for the Open World.

[BibT_eX]

[DOI]

CoRR, 2022

The Untapped Potential of Off-the-Shelf Convolutional Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

Multi-modal Aerial View Object Classification Challenge Results - PBVS 2022.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

2021

Advancing the Design and Utility of Adversarial Machine Learning Methods.

[BibT_eX]

[DOI]

Nathan Inkawhich

PhD thesis, 2021

Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data.

[BibT_eX]

[DOI]

IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2021

Training SAR-ATR Models for Reliable Operation in Open-World Environments.

[BibT_eX]

[DOI]

IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2021

Fine-grained Out-of-Distribution Detection with Mixup Outlier Exposure.

[BibT_eX]

[DOI]

CoRR, 2021

Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

NTIRE 2021 Multi-Modal Aerial View Object Classification Challenge.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020

DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Transferable Perturbations of Deep Feature Distributions.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Feature Space Perturbations Yield More Transferable Adversarial Examples.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers.

[BibT_eX]

[DOI]

CoRR, 2018

Nathan Inkawhich

Timeline

Legend:

Links

On csauthors.net:

Bibliography

Loading...